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Journal Article MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems
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Authors
Yongseok Choi, Eunji Lim, Jaekwon Shin, Cheol-Hoon Lee
Issue Date
2021-11
Citation
Electronics, v.10, no.21, pp.1-18
ISSN
2079-9292
Publisher
MDPI
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.3390/electronics10212720
Abstract
Large-scale computational problems that need to be addressed in modern computers, such as deep learning or big data analysis, cannot be solved in a single computer, but can be solved with distributed computer systems. Since most distributed computing systems, consisting of a large number of networked computers, should propagate their computational results to each other, they can suffer the problem of an increasing overhead, resulting in lower computational efficiencies. To solve these problems, we proposed an architecture of a distributed system that used a shared memory that is simultaneously accessible by multiple computers. Our architecture aimed to be implemented in FPGA or ASIC. Using an FPGA board that implemented our architecture, we configured the actual distributed system and showed the feasibility of our system. We compared the results of the deep learning application test using our architecture with that using Google Tensorflow's parameter server mechanism. We showed improvements in our architecture beyond Google Tensorflow's parameter server mechanism and we determined the future direction of research by deriving the expected problems.
KSP Keywords
Big Data analysis, Deep learning application, Distributed System(DS), FPGA Board, Google TensorFlow, Parameter server, Shared Memory, computational problems, computational results, deep learning(DL), distributed computer systems
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
CC BY